报错代码：
with tf.Session() as sess:
sess.run(init_op)for i in range(self.epoch_num):
batch_images, batch_labels = mnist.train.next_batch(self.batch_size)
batch_images = tf.reshape(tensor=batch_images, shape=[self.batch_size, 28, 28, 1])
batch_images = tf.image.resize_images(images=batch_images,size=(32,32))
print("images shape:{}".format(batch_images.shape))
print("labels shape:{}".format(batch_labels.shape))
accuracy = sess.run(train_op, feed_dict={images_holder:batch_images, labels_holder:batch_labels})
print(accuracy)报错信息：
TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, numpy ndarrays, or TensorHandles.For reference, the tensor object was Tensor("resize_images/ResizeBilinear:0", shape=(100, 32, 32, 1), dtype=float32) which was passed to the feed with key Tensor("x:0", shape=(100, 32, 32, 1), dtype=float32).
【TensorFlow 报错 TypeError: The value of a feed cannot be a tf.Tensor object】报错提示已经很明显了，就是喂给训练操作的数据不能是张量，只能是 scalars, strings, lists, numpy ndarrays, or TensorHandles
解决方法：
在喂给训练操作的张量后面加 .eval()，将张量操作算出来的结果喂给训练操作就正常了
accuracy = sess.run(train_op, feed_dict={images_holder:batch_images.eval(), labels_holder:batch_labels})
